BIST 500: Department Seminar (0 credit)Semesters Offered: Fall & SpringFaculty: Korostyshevskiy, ValeriyDescription: The Department of Biostatistics, Bioinformatics and Biomathematics invites experts to Georgetown University to make presentations on topics of interest in the fields of biostatistics, epidemiology or computational biology. Speakers may discuss recently completed or early-stage research that they have taken on or describe other types of qualified activity. Topics often align with research directions within the Department, but also may correspond to areas of interest to the Lombardi Comprehensive Cancer Center and the Department of Biostatistics, Bioinformatics and Biomathematics. Essential to uniting the purpose of the Bio3 Seminar Series is a diversity of topics throughout each semester.

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BIST 501: Introduction to Biostatistics: Experimental Design and Analysis (3 credits)Semesters Offered: NOT available for 2018-2019Faculty: Dragomir, AncaDescription: This course is designed for introductory biostatistical theory and application for students pursuing a master's degree in fields outside of the Department of Biostatistics, Bioinformatics, and Biomathematics. Students first learn the four pillars of exploring and displaying data appropriately, exploring relationships between two variables, issues of gathering sample data, and understanding randomness and probability. On these pillars, students then can develop the platform for statistical inference including proportions and means, multiple regression, and ANOVA.

BIST 505: Epidemiology & Public Health (3 credits)Semesters Offered: SpringFaculty: Loffredo, Chris & Dash, ChiranjeevDescription: Epidemiology is the scientific discipline of public health. It therefore plays a central role in the identification, characterization, and control of risk factors for human diseases. The course will begin with an overview of the history of epidemiology, followed by consideration of chronic and acute disease rates by time, place, and person, and how the major types of epidemiological study designs (cross-sectional, case-control, cohort, and randomized trials) address these public health concerns. The course will provide information on the basic methods of analysis associated with the study design, with an emphasis on critically reading and evaluating the epidemiological literature. Special topics, such as screening studies, cancer epidemiology and prevention, and infectious diseases will also be introduced. This course includes lectures and discussion sessions.

BIST 512: Categorical Data Analysis (3 credits)Semesters Offered: SpringFaculty: Ahn, JaeilDescription: This course covers theory and methods for the analysis of categorical data. The main subject areas are analysis of contingency tables, chi-square and exact tests, logistic models under binomial and multinomial sampling, log-linear models under Poisson sampling and their applications to perform contingency table analysis for nominal and ordinal variables. Methods of maximum likelihood estimation and goodness of fit procedures are discussed. Generalized linear model will be heavily utilized with an emphasis on model building and interpretation. Examples will be illustrated in R. Students are expected to use R or SAS when necessary, for all homework assignments.

BIST 513: Survival Analysis (3 credits)Semesters Offered: SpringFaculty: Fang, HongbinDescription: The course will introduce basic concepts in the analysis of survival data. It will be oriented toward application and interpretation of various methodologies. Examples will be drawn mostly from medical and epidemiologic research.

BIST 515: Introduction to Statistical Software (2 credits)Semesters Offered: FallFaculty: Zhong, Xiaogang (Simon)Description: BIST 515 is an introductory course to the open-source programming language R and the popular statistical software SAS. Basic syntax and simple applications to Biostatistics and Bioinformatics will be presented.

BIST 532: Machine Learning for Bioinformatics (3 credits)
Semesters Offered: FallFaculty: Li, JamesDescription: This course is a combination of theories and empirical skills on managing, processing and analyzing high-throughput biomedical data generated from a variety of "Omics" technologies, which spans genomics, trascriptomics, proteomics, and metabolomics. It introduces the students to the conceptual and experimental background, together with specific guidelines for handling raw data. Hand-on skills with R/Bioconductor and other software tools will be covered on popular "Omics" applications, such as microarray gene expression profiling, mass spectrometry-based metabolomics, RNA-seq, pathway analysis, and etc.Prerequisites: BIST 511, BIST 512, BIST 514

BIST 540: Experimental Design/Clinical Trials (3 credits)Semesters Offered: FallFaculty: Wang, HongkunDescription: The objective of the course is to explain in practical terms the basic principles of clinical trials, with particular emphasis on their scientific rationale, organization and planning, and methodology. Issues discussed include design of randomized and non-randomized trials, size of a clinical trial, monitoring of trial progress, and some basic principles of statistical analysis. The intent is to present the methodology of clinical trials with emphasis on the practical aspects.

BIST 545: Quantitative Data Analysis & Reporting (2 credits)Semesters Offered: FallFaculty: Korostyshevskiy, Valeriy
Description: The goal of this course is to enhance students’ skills for identifying appropriate choices of statistical methodologies; and developing statistical approaches to applied research problems and effective communication of the approaches and findings. The course will give students hands on experience in statistical applications. The course will be organized around a series of case studies based on applied problems from different sources with a focus on application of statistical techniques as opposed to the subject matter of the data at hand. Students will formulate statistical approaches to the applied research problems; perform exploratory data analysis, model building and statistical inference; write reports, make oral presentations of results of analyses and interactively discuss analysis aspects of the case studies.

BIST 595: Methods for Biomedical Data Science (2 Credits)Semesters Offered: FallFaculty: Li, JamesDescription: As a recent hot topic, big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time, and therefore, specialized and advanced paradigms, architectures, and analytical methodologies are necessary. Biomedical field is one of the most popular areas that generates big data which are typically collected from multiple sources and distributed from multiple sites. Statistical and computational skills are essential to analyze and extract knowledge from massive data. This course is designed for graduate students looking to acquire additional statistical and computational concepts, theories as well as skills beyond other informatics course(s) in the BIST curriculum. The course is divided into 4 modules with each module focusing on a specialized topic in biomedical data science taught by faculty with research interests and expertise in that specific research area. The goal is to introduce students to the use of cutting edge methodologies and tools in biomedical data science that have current broad applications on processing biomedical research and health care data.

BIST 817: Special Topic I (1 Credit)Semesters Offered: Spring (starting Spring 2019)Faculty: Department FacultyDescription: This course is designed to enrich students' background by exposing them to advanced methodologies, cutting-edge techniques, and other material not generally covered in regular curriculum. Examples include (but not limited to): adaptive design of clinical trials, Bayesian analysis, spline regression, meta-analysis. Students will be involved in both theory and hands-on exercises.Prerequisites: BIST 510, 511, 515 or Instructor's permission.

BIST 818: Special Topic II (1 Credit)Semesters Offered: Fall (starting Fall 2019)Faculty: Department FacultyDescription: This course is designed to enrich students' background by exposing them to advanced methodologies, cutting-edge techniques, and other material not generally covered in regular curriculum. Examples include (but not limited to): adaptive design of clinical trials, Bayesian analysis, spline regression, meta-analysis. Students will be involved in both theory and hands-on exercises.Prerequisites: BIST 510, 511, 515 or Instructor's permission.

BIST 917: Practicum (1 Credit)Semesters Offered: SpringFaculty: Department FacultyDescription: Students will be involved in a research project under the supervision of a faculty member. While the consulting class will expose them to short-term projects, the practicum will provide them with an opportunity to implement a combination of the skills they have acquired and to extend them in a limited context. This practical experience should span 3-4 months. The project will be written up as a Master’s paper including the following sections: background to the problem, experimental design, and description of the data, analytical methods, results, and interpretation of the latter. This paper will be defended orally, after no fewer than two faculty members (the advisor and one other) have read it and deemed it ready for presentation.

BIST 918: Practicum (1 Credit)Semesters Offered: FallFaculty: Department FacultyDescription: Students will be involved in a research project under the supervision of a faculty member. While the consulting class will expose them to short-term projects, the practicum will provide them with an opportunity to implement a combination of the skills they have acquired and to extend them in a limited context. This practical experience should span 3-4 months. The project will be written up as a Master’s paper including the following sections: background to the problem, experimental design, description of the data, analytical methods, results, and interpretation of the latter. This paper will be defended orally, after no fewer than two faculty members (the advisor and one other) have read it and deemed it ready for presentation.

PH.D. LEVEL COURSES (bist 610 - 665)

BIST 610: Probability & Large Sample Theory (3 Credits)Semesters Offered: FallFaculty: Fan, RuzongDescription: This is a course for Ph.D students with advanced knowledge of probability, statistics, and mathematics. The class covers both advanced probability theory and basic theory of stochastic processes to facilitate research of biostatistics and biomedical sciences. For probability theory, the following topics will be taught: measures, integration, probability, large sample theory of random variables. For stochastic processes, an introduction of martingales and point processes with applications to survival analysis will be taught.

BIST 625: Statistical Computing (3 Credits)Semesters Offered: SpringFaculty: Zhong, Xiaogang (Simon)Description: This course will cover a wide range of topics that are likely to be of use to a graduate student or researcher who needs to use and develop statistical methods. We will concentrate on optimization, ideas from numerical linear algebra, numerical integration and Monte Carlo Methods in R and C. It is a survey of special topics that you will nd useful as you pursue PhD degrees in our Department. These include interfacing R and C, exploring methods of computational statistics, and developing strategies for tackling computational problems in statistics.

BIST 635: Longitudinal Data Analysis (3 Credits)Semesters Offered: SpringFaculty: Wu, ColinDescription: This course intends to cover the major parametric and nonparametric models, estimation methods and inferences for the analysis of longitudinal or clustered data, i.e., repeated measurements data. The main topics include most of the well-known parametric, semiparametric and nonparametric regression models and their corresponding estimation and inference procedures. The regression models include the parametric marginal models, the linear and generalized linear mixed-effects models, the partially linear semiparametric models, and the structured nonparametric regression models. The estimation and inferences include the likelihood-based procedures, the kernel and basis approximation based nonparametric smoothing methods, the resampling subject bootstrap, and the asymptotically approximated inferences for longitudinal data. The practical aspects of the course will focus on the different longitudinal/clustered data structures, model construction and interpretations, computationally feasible estimation and inference methods, and applications to real longitudinal studies. Theoretical justifications of the estimation and inference methods will be outlined to show the unique techniques of asymptotic developments for repeated measurements data. But the detailed theoretical derivations will be assigned as reading materials. Students are expected to analyze some real datasets from biomedical studies using different models, conduct sensitivity studies and interpret the results. The main objective of the course is for students to build a solid background of the regression methods for longitudinal/clustered data and be able to apply these tools in practice. The R packages for longitudinal data and smoothing methods will be used throughout the course.